Flower quality control control app with AI QC management: 

Flower quality control control for flower stem & bouquets packers, wholesale, flower exporters. Full flower business management from quality to grower payments to labels and export documentation.   Flower quality control control app with AI QC management: quality controls for all flowers products for flower packers, exporter, wholesalers. Reduce quality control costs. Eliminate waste, price negotiations, and QC mistakes. Maximize quality consistency.


Flower quality control control app with AI QC management: 

Flower quality control control for flower stem & bouquets packers, wholesale, flower exporters. Full flower business management from quality to grower payments to labels and export documentation. Flower quality control control app with AI QC management: quality controls for all flowers products for flower packers, exporter, wholesalers. Reduce quality control costs. Eliminate waste, price negotiations, and QC mistakes. Maximize quality consistency.  Features below require Farmsoft Fresh Produce, Food Service, Meat Packing

AI powered Flower quality control

Optionally use FarmsoftQC AI powered quality control:  take a photo of the fruit and let FarmsoftQC fill out the control for you.  Fast, consistent, accurate AI powered Quality Control.

Flower Stock-take quality control

Perform quality control stock-takes any time by category or storage location.  Know how much  inventory and its quality in real time.  


Quality control for farm tasks, farm equipment (tractors, spray rig etc), in field fresh produce QC tests. (Requires Farmsoft Farm Management app)

Flower Quality control during shipping

Perform optional quality control tests on fresh produce prior to shipping, or during the container loading phase.  

Flower Traceability & recalls

Mock recalls up and down supply chain.   Reduces fresh produce food safety compliance costs, makes audits easy. Optional fresh produce blockchain by CHAIN-TRACE.COM

Perform Flower quality controls by scanning labels / RFID

Scan a pallet label, inventory label, or even PO/Invoice/BOL to perform a quality control.  Saves time and increases accuracy.  
Quality Control tests can be recalled back to a specific invoice, supplier, batch, etc...

In order to improve the accuracy and consistency of control phytomedicine preparations worldwide, regulatory authorities are requesting research into new analytical methods for the stricter standardisation of phytomedicines. Such methods have to be both objective and robust, and should address the reproducibility of the content of the chemical profiles. NMR-based metabolomics, which combines high-resolution 1H-NMR spectroscopy with chemometric analysis, has been employed as an innovative way to meet those demands. In this paper, chamomile flowers from three different geographical regions, namely, Egypt, Hungary and Slovakia were characterised using 1H-NMR spectroscopy followed by principal component analysis. It was found that the origin, purity and preparation methods contributed to the differences observed in prepared chamomile extracts. In addition, this method also enabled the elucidation of the molecular information embedded in the spectra responsible for the observed variability. The metabolomic strategy employed in the current study should provide an efficient tool for the quality control and authentication of phytomedicines.

Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD
•  Quality by Design concept was conducted for NIR model design.

•  Strong synergic interactions among model parameters were discovered by QbD.

•  Spectral assignment was used to select variable instead of chemometric method.

•  A more robust model was established by spectral assignment combined with D-optimal.

Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial applications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with Radj2 increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an appropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher Rpre2 and a lower RMSEP. When the latent factors of PLS was small, Rpre2 of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT.

Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy [1]. It not only has a specific efficacy of detoxification, but also could be used as a heat-clearing drink. It has even been developed into products, such as Chinese famous tea drink Wang Laoji, Jiaduobao, as well as the distilled liquid of Honeysuckle flower. The annual sales of Honeysuckle flower productions are among the best in China. For example, Jiaduobao's operating income in 2016 was 24 billion yuan, ranking first in the Chinese herbal tea industry with a market share of 52.6%. In Japan, the Kobayashi's Qingfei Soup is an edible-medicinal prescription containing Honeysuckle flower.

Ethanol precipitation is a characteristic and significant process of Honeysuckle flower production, which calls for a precise quality control method. Off-line quality control methods have hysteresis leading to an insecure and unpredictable production quality [2]. To solve this issue, process analytical technology (PAT) based on chemometrics is proposed to quality control, which is especially applicable in case of complex processes [3]. Currently, NIR spectroscopy is the most commonly used PAT process analyser in pharmaceutical technology because of non-destructive measurements and real-time monitoring in process [4,5]. It is especially suitable for a complex production, which needs process quality control [6,7].

Wu et al. used NIR spectroscopy to monitor the concentration distribution of amino acids in the hydrolysis of Cornu Bubali [2]. Xu et al. proposed a multi-phase and multivariate statistical process control strategy for alcohol precipitation of Honeysuckle flower. [8]. Laub-Ekgreen et al. applied NIR spectroscopy to rapid and non-destructive salt concentration monitoring in the pickling process of squid [9]. Oxidative damage of pork myofibrils during frozen storage has been monitored by the NIR hyperspectral imaging [10].

In the application of NIR to process quality control, there is an essential factor, quantitative model. To establish an accurate NIR model, the most important part is the optimization of the critical modeling parameters (CMPs). One CMP in NIR modeling is the spectral preprocessing because of some interfering information [11]. Pizarro et al. and Christensen et al. both demonstrated the performance of quantitative NIR models established by different pre-processing methods were diverse [12,13]. Variable selection [14] is another CMP to extract useful information for modeling. Bi et al. proved that, compared with the full spectra, the NIR model established by optimal spectra achieved better performance [15]. Yuan et al. indicated that the discriminant models were improved and simplified significantly by variable selection [16]. In addition, a suitable latent factor is also a CMP to avoid over-fitting and under-fitting for modeling [17].

In classical modeling, the CMPs were optimized step-by-step. Genetic algorithm is a commonly used method to optimize the spectral pre-processing method or variable selection method [18]. Rosas et al. compared three spectral pre-processing methods for NIR process optimization of a multicomponent formulation [19]. Wu et al. used a novel method to optimize the model performance of Partial least square (PLS), interval PLS (iPLS), backward interval PLS (BiPLS) and moving window PLS (MWPLS), and point out that with different evaluation indicator, the optimal method is diverse [20]. Pan et al. found that BiPLS was the appropriate variable selection method for establishing the particle size model rather than synergy iPLS (SiPLS) [21].

Nevertheless, the established models optimized step-by-step ignored the interaction among modeling parameters and were not the best in overall situation. An integrated approach was introduced to optimize several modeling parameters simultaneously based on genetic algorithm [22,23]. Similarly, a systematic modeling method was put up by using a processing trajectory to select modeling parameters [[24], [25], [26]]. Although more valid than before, this method still needs to establish a lot of models laboriously and could not demonstrate the interaction among the parameters. Hence, modeling design is necessarily applied here to simplify the process and establish an overall optimal model.

To implement modeling design, Quality by Design (QbD) concept is a good choice [27], which was introduced in chemical manufacturing control in 2004. In the ICH Q8 guideline, QbD is defined as a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding, as well as process control, based on sound science and quality risk management [28]. It was often used to optimize process parameters in pharmaceutical industry [29]. Liu et al. used it to the quality control of Angong Niuhuang Wan by Laser-Induced Breakdown Spectroscopy [30]. Dai et al. applied it to the development of a novel RP-HPLC analytical method for Huanglian [31]. Similarly, it could also be applied to optimize NIR CMPs by a design of modeling evaluation procedures.

However, the chemometrics variable selection could not discern special components in samples directly. Lee et al. argued that the different variable selection methods performed wide variability in their capabilities to identify the consistent subset of variables [32]. Du et al. also demonstrated that different chemometrics selection methods led to distinct characteristic wavelengths and bands [33]. NIR spectral assignment based on the interrelation between spectra and structure is efficacious to improve model performance and interpretation [34,35]. Chlorogenic acid is the main medicinal component of honeysuckle [36,37]. It is also used as the quality control component of honeysuckle in Chinese Pharmacopeia. Many researches proved that it played an important role in the treatment of SARS virus in 2003 and novel coronavirus pneumonia in 2019.

Therefore, a design of NIR modeling evaluation procedures was implemented by D-optimal design method according to QbD concept. Furthermore, getting the characteristic band of chlorogenic acid [38], the special component of Honeysuckle flower, by spectral assignment, this paper creatively combined this characteristic band with modeling CMPs designed by D-optimal to establish a more precise and reliable model. These also provided a reference method for modeling design and the establishment of global optimal models in PAT of edible-medicinal food.

Aroma profiles and volatile profiles were established based on volatile and aroma-active components identified by gas chromatography–mass spectrometry and olfactometry (GC–MS–O) and electronic nose (E-nose). The two profiles were used for quality control and origin identification of chrysanthemum flower teas. Results showed that 86 volatile components were identified in five chrysanthemum flower teas, including terpenes, alcohols, ketones, aldehydes, esters and others. Of them, 33 aroma-active components were recognised, including 10 aroma categories. The aroma profiles and volatile profiles were established by 10 aroma categories and E-nose. Chrysanthemum flower teas were divided into five groups on PCA score plots based on their aroma profiles and volatile profiles, and the key volatile (aroma-active) components resulting in the tea sample differences were determined. The quality of chrysanthemum flower teas could be evaluated according to aroma-active components and aroma profiles and origins could be discriminated by PCA combined with GC–MS–O and E-nose.

Parts of Salvia species such as its flowers and leaves are currently used as a culinary herb and for some medicinal applications. To distinguish the different sage extracts it is necessary to analyze their individual chemical compositions. Their characteristic compounds might be established as markers to differentiate between sage flowers and leaf extracts or to determine the manufacturing technology and storage conditions. Tri-p-coumaroylspermidine can be detected only in flowers and has been described here for Salvia and Lavandula species for the first time. Markers for oxidation processes are the novel compounds salviquinone A and B, which were generated from carnosol by exposure to oxygen. Caffeic acid ethyl ester was established as an indirect marker for the usage of ethanol as extraction solvent. The compounds were identified by LC-QTOF-HRESIMS, LC-MS, NMR, IR, and single-crystal X-ray diffraction after isolation by semipreparative HPLC. Furthermore, sage flower resin showed interesting antibacterial in vitro activities against Gram-positive and Gram-negative bacteria.

Every member of our QC (Quality Control) staff is dedicated to providing you with consistent, high quality flowers all year round. We follow a demanding set of quality parameters in the production, harvesting, and packaging of each and every flower. In addition to controls at the farm, the quality control specialists at our Miami distribution center assess all our incoming shipments of flowers each day to make sure that they meet our quality, grading, and packing
These controls are processed through an innovative software system called Petals, which was created by our own in-house IT and QC departments. This unique software allows us to database all the controls and immediately
communicate and connect to our farms as soon as product is received into our Miami facility. These standards and processes act as your insurance that every flower is delivered to your door as though it were just cut at the farm.

Our Quality Control live stream is currently unavailable.

Each and every flower we cut must meet exact grading standards.

We pack flowers in a manner that maintains their freshness and quality during transit.

Refrigerated shipping and handling at every step of the delivery process increases the life and health of our flowers.

We guarantee the freshest flowers
you can buy.


Every variety is different, and each should be cut at a different time to yield the best
possible bloom for the end consumer.
Each grade and variety is measured against its ideal bud size.
Each stem is conveniently pre-processed with 12.5 cm of foliage stripped.
All stems are cut evenly within each bunch to ensure proper hydration.


We measure the stem lengths without including the bloom heights.
We use a two-tier bunching system and grade all of our roses using the bottom of the lower tier.
When adding the bloom height from the top tier, on average, an additional height of 10 cm is included.
Refrigerated shipping and handling at every step
of the delivery process increases the life and
health of our flowers. As soon as the stems are
cut at the farm, we transport them in refrigerated
cargo trucks and airplanes and only handle them
in refrigerated facilities.


Flowers are packed in multiple rows for efficiency and protection.
Rubber bands on the stem are placed above the bottom of the bunch so that they are not cut when the stems are processed. A large rubber band is placed around the flowers to hold the bunch intact.
Bunches are placed into sleeves for protection. The sleeves are labeled with the variety name, grade, and farm code for your convenience.
Bunches are meticulously packed in the box using cushion covers, placed in key areas to prevent damage during transit. Plastic straps are used to hold the bunches in place.